non-co greenhouse gas emissions data for …gtap/epa project, including gtap datasets for fossil...
TRANSCRIPT
Non-CO2 Greenhouse Gas Emissions Data for Climate Change Economic Analysis*
by
Steven K. Rose U.S. Environmental Protection Agency, Washington, DC, USA
Huey-Lin Lee
National Institute for Environmental Studies, Tsukuba, Japan
GTAP Working Paper No. 43 2008
*Chapter 5 of the forthcoming book Economic Analysis of Land Use in Global Climate Change Policy, edited by Thomas W. Hertel, Steven Rose, and Richard S.J. Tol
2
Table of Contents 1. Introduction ..................................................................................................................3 2. Background ..................................................................................................................4
3. Methodology ..............................................................................................................10 3.1 USEPA NCGG emissions input data .........................................................................10 3.2 Mapping USEPA NCGG data to GTAP ....................................................................11 3.3 Mapping to GTAP emissions drivers .........................................................................17 4. NCGG Data Overview ...............................................................................................19
5. Conclusion ..................................................................................................................20
6. References ..................................................................................................................27 Table 1. Non- CO2 greenhouse gases included in the database and their 100-year global warming potential (GWP) (IPCC, 1996) ...........................5 Table 2. 2001 global land-use related NCGG emissions ...........................................7 Table 3. Mapping NCGG categories and subcategories to GTAP v6 sectors and emissions drivers ........................................................................................15 Figure 1. 2001 global land-use related shares of NCGG emissions .............................6 Figure 2. 2001 global NCGG emissions by sector and gas (MtCeq) .........................22 Figure 3. 2001 global NCGG emissions by region and gas (MtCeq) .........................23 Figure 4. 2001 United States NCGG emissions by sector and source (MtCeq) .........24 Figure 5. 2001 China NCGG emissions by sector and source (MtCeq) .....................25 Figure 6. 2001 United States NCGG emissions by sector and emissions driver type (MtCeq) ...............................................................................................26 Appendix A. GTAP sectoral classification ................................................................29 Table A1. GSC2 Sectors defined by Reference to the Provisional CPC ............................29 Table A2. GSC2 Sectors defined by Reference to the ISIC, Rev. 3 ..................................32 Appendix B. Regions in the GTAP 6 Data Base and Mapping to Standard Countries ..............................................................................................35
3
NON-CO2 GREENHOUSE GAS EMISSIONS DATA FOR CLIMATE CHANGE ECONOMIC ANALYSIS
Steven K. Rose and Huey-Lin Lee
1. Introduction
Non-CO2 (carbon dioxide) greenhouse gas emissions (NCGGs) are responsible
for almost a third of historic radiative forcing, and land related activities contribute
approximately two thirds of global NCGG emissions. Therefore, modeling of NCGG
emissions is essential for projecting climate change and evaluating the net environmental
effectiveness of alternative climate change mitigation strategies.
This chapter describes the GTAP NCGG emissions dataset. It highlights NCGG
emissions associated with land-based activities, and the heterogeneity of sectoral and
regional NCGG emissions. The NCGG dataset complements the GTAP fossil fuel
combustion CO2 emissions database (Lee, 2005) and the forest carbon stock dataset,
where the later is described in chapter 21 of this volume. Together, the datasets provide a
fairly complete GHG emissions and carbon sink profile for each sector within each
region.
The GTAP NCGG emissions data were derived from new highly disaggregated
country-level emissions source data from the United States Environmental Protection
Agency (USEPA) (Rose et al., 2007b). Unlike other NCGG databases, the data was
specifically developed for direct integration with economic activity datasets. The detailed
USEPA source emissions data and the explicit linking of NCGG emissions directly to
1 GTAP Working Paper No. 40
4
emissions drivers (e.g., energy use, land use, fertilizer, capital) during the mapping to the
GTAP economic activity dataset, allows for more explicit, realistic, and internally
consistent modeling of emissions activity and mitigation technologies and costs. The
NCGG dataset was collaboratively developed by USEPA and Purdue University’s Global
Trade Analysis Project (GTAP). The most current version of the dataset is publicly
available on the GTAP website (https://www.gtap.agecon.purdue.edu/).
2. Background
NCGGs include nitrous oxide (N2O), methane (CH4), and fourteen fluorinated
gases (F-gases) (Table 1).2 These greenhouse gases (GHGs), along with carbon dioxide,
are referred to as the Kyoto basket of greenhouse gases. Like CO2, NCGGs are gases that
trap heat in the Earth’s atmosphere. They trap more heat per molecule than CO2. NCGGs
were responsible for 30% of radiative forcing between pre-industrial times and 1990
(IPCC, 2001). USEPA (2006a) projects NCGG growth of 44% from 1990 to 2020, with
methane two thirds of 1990 emissions and growing by 35%, and nitrous oxide just under
a third of 1990 emissions growing by 41%, while the F-gases in total represent
approximately 3% of 1990 emissions growing by almost 300% to become 7% of NCGG
emissions by 2020.3
2 In the database, the fourteen F-gases are grouped into four representative groups: CF4 (Perfluoromethane), HFC-134a (Hydrofluorocarbons, C2H2F4), HFC-23 (Hydrofluorocarbons, CHF3), SF6 (Sulphur hexafluoride). 3 Based on carbon dioxide equivalent units computed using the IPCC Second Assessment Report 100-year global warming potentials for reporting inventories (IPCC, 1996).
5
Table 1. Non- CO2 greenhouse gases included in the database and their 100-year global warming potential (GWP) (IPCC, 1996)
Gas GWP Carbon dioxide (CO2) 1Methane (CH4) 21Nitrous oxide (N2O) 310HFC-23 11,700HFC-32 650HFC-125 2,800HFC-134a 1,300HFC-143a 3,800HFC-152a 140HFC-227ea 2,900HFC-236fa 6,300HFC-4310mee 1,300CF4 6,500C2F6 9,200C4F10 7,000C6F16 7,400SF6 23,900
Land use and land based practices represent an important driver of NCGG
emissions. In 2000, agricultural land related activities were estimated to produce
approximately 50% of global atmospheric methane (CH4) emissions and 75% of global
nitrous oxide (N2O) emissions. This amounts to a total contribution to all anthropogenic
greenhouse gas emissions in 2000 of approximately 14% on a carbon dioxide equivalent
basis (USEPA, 2006a). By tying NCGG emissions directly to economic activities, as is
done with the dataset described in this chapter, we have an explicit characterization of
emissions associated with economic sectors and an economic structure for modeling
NCGG emissions. In Figure 1 and Table 2, we see that land related economic sectors are
responsible for 60% of global NCGG emissions, with ruminant livestock production
contributing the largest share at 25%, and paddy rice second at 8%, followed closely by
various crops and non-ruminant livestock.
6
Figure 1. 2001 global land-use related shares of NCGG emissions
Paddy rice8%
Bovine cattle, sheep and goats, horses
25%
Animal products nec5%
Raw milk5%
Forestry0%
Other sectors40%
Cereal grains3%
Fruits, vegetables, nuts6%
Sugar cane, sugar beet1%
Plant-based fibers1%
Oil seeds2%
Wheat2%
Crops nec2%
7
Table 2. 2001 global land-use related NCGG emissions
NCGGs are important because of both their historic and projected contributions to
radiative forcing and climate change, as well as for their climate change mitigation
potential, especially as alternatives to fossil fuel combustion CO2 emissions mitigation.
Previous engineering-based studies and project experience through government programs
has identified a variety of viable NCGG mitigation technologies and provided estimates
of direct project net costs (e.g., USEPA, 2006b). Furthermore, macroeconomic studies
have found that NCGG mitigation opportunities offer mitigation flexibility that could
lower the costs of achieving emissions reduction quantity objectives, such as for national
commitments, cap-and-trade programs, and long-run climate change stabilization (e.g., de
la Chesnaye and Weyant, 2006). In addition, Rose et al. (2007a) reports results explicitly
isolating potential cost-effective roles for land-based NCGG mitigation, as well as forest
sequestration and bioenergy, in dynamic climate change stabilization mitigation
portfolios. Meanwhile, public-private partnerships have identified and developed
profitable NCGG reduction partnerships (e.g., USEPA’s Methane to Markets program,
Sector MtCeqPaddy rice 199Wheat 57Cereal grains 67Fruits, vegetables, nuts 150Oil seeds 49Sugar cane, sugar beet 18Plant-based fibers 31Crops nec 55Bovine cattle, sheep and goats, horses 609Animal products nec 136Raw milk 133Forestry 0TOTAL land-use related sectors 1505Other sectors 976
8
http://www.epa.gov/methanetomarkets/). Research results and hands-on experience like
these have justified the inclusion of NCGG mitigation alternatives in international
programs such as the UNFCCC Joint Implementation and Clean Development
Mechanism Programs, as well as their explicit inclusion in recently proposed U.S.
legislation.
Despite all this, sector-level and economy-wide NCGG emissions and mitigation
modeling is still relatively unsophisticated. In large part, because modelers have focused
their efforts on modeling energy and industrial fossil fuel CO2 emissions based on fuel
combustion (Hourcade et al., 2001). As that modeling has advanced and global NCGG
emissions and cost data have become available, the modeling community has shifted its
attention to the other categories of emissions—NCGGs, non-combustion CO2, and land-
use and land-use change CO2. The initial modeling, built off aggregated databases and
aggregated and partially integrated representations of mitigation responses, established
that NCGG mitigation could be a substantial part of a cost-effective strategy (de la
Chesnaye and Weyant, 2006). However, more explicit evaluation of NCGG mitigation
technologies and the impact of NCGG mitigation decisions within and across sectors and
regions calls for more disaggregated consistent emissions source data that is integrated
more directly with the economic activity generating the emissions.
The GTAP NCGG database was developed to fill this need and facilitate more
refined modeling and evaluation of NCGG emissions and mitigation potential. For each
region, the dataset provides disaggregated source-level NCGG emissions for each
economic sector and regional household. Furthermore, the sector emissions are tied to
emissions drivers: factor inputs (endowments), intermediate inputs, or output. Household
9
emissions are tied to intermediate input use, specifically energy use. The NCGG
emissions are reported in terms of the 87 GTAP regions, 57 sectors, and regional
households associated with version 6 of the GTAP database.
The NCGG database is one part of a GTAP/EPA development effort designed to
improve international climate modeling by developing key climate related datasets that
are both internally consistent and integrated with core economic activity datasets. A
number of complementary resources are currently available, some products of the
GTAP/EPA project, including GTAP datasets for fossil fuel combustion CO2 emissions,
land-use and land-cover, forest carbon; and USEPA datasets for country-level historical
and near-term NCGG projections, and NCGG emissions abatement costs estimates. See
Rose et al. (2007c) for an overview of these resources. Furthermore, development efforts
are on-going that will yield additional GTAP/EPA products and improvements in the
future. Additional data products will include a global soil carbon dataset and, as
discussed below, incorporation of additional emissions categories, including non-fossil
fuel combustion CO2 emissions, as well as additional biomass burning and biomass
combustion CO2 and non-CO2 emissions.
The remainder of this chapter is organized as follows. The next section describes
the methodologies employed in developing the GTAP NCGG dataset. The remaining
sections provide an overview of the data and discuss modeling opportunities. Land-based
NCGG emissions are emphasized throughout.
10
3. Methodology
This section describes the NCGG input data for the GTAP NCGG dataset and the
methods employed in mapping the data. Each NCGG emissions source (subcategory)
from the input data set for each country was allocated to the corresponding GTAP
sector(s) or regional household and then directly to an appropriate unique economic
activity emissions driver within each sector/household. This methodology ensures that
GTAP NCGG emissions totals are consistent with the original sources, while emissions-
driver relationships are customized to the economic model structure.
3.1 USEPA NCGG emissions input data
The US Environmental Protection Agency (USEPA) developed a detailed non-
CO2 and non-fossil fuel combustion CO2 (“Other CO2”) greenhouse gas emissions
database specifically for use by global economic models (Rose et al., 2007b). The
dataset’s disaggregated emissions structure maps directly to countries and economic
sectors and facilitates utilization of available input activity quantity data, such as energy
volumes and land-use acreage in both the mapping of emissions into GTAP as well as
emissions modeling.
Other global emissions datasets have provided valuable regional and global
estimates (e.g., USEPA, 2006a; Olivier, 2002); however, estimated emissions have been
developed and presented according to IPCC source categories that aggregate across
countries, and more importantly, aggregate across economic sectors and activities;
thereby, making it difficult to model actual emitting activities and abatement strategies.
The Rose et al. (2007b) NCGG emissions categories and subcategories are also based on
11
IPCC emissions inventory categories and subcategories (IPCC, 1997a); however, the data
is substantially more disaggregated than other datasets. The 2001 base year of the new
dataset corresponds to the base year of the GTAP version 6 database. The database
provides emissions for 29 non-CO2 and Other CO2 GHG emissions categories with 153
unique emissions sources (subcategories) for 226 countries. The other datasets provide
emissions for more aggregated regions and do not provide emissions by subcategories.
Annex 1 country emissions were extracted from national UNFCCC Common Reporting
Framework and National Inventory submissions. Non-Annex 1 country emissions were
primarily drawn and, when possible, disaggregated from available National Inventories.
When National Inventories were not available or specific emissions categories were not
represented, other data sources and methods were called upon: the EDGAR 3.2 database
by RIVM/TNO4 (biomass burning, Other CO2), ALGAS country reports;5 or, estimated
using IPCC inventory methods or extrapolated from 2000 estimates. See Rose et al.
(2007b) for more detailed descriptions of the methods used in developing the data in each
of the USEPA NCGG emissions subcategories.
3.2 Mapping USEPA NCGG data to GTAP
Table 3 provides a summary of the emissions categories and subcategories
represented in the GTAP NCGG dataset. Most, but not all, of the USEPA categories and
subcategories were mapped into GTAP. Specifically, 24 categories and 119 subcategories
4 EDGAR (Emission Database for Global Atmospheric Research), Version 3.2 (Olivier, 2002) 5 ALGAS (Asia Least-Cost Greenhouse Gas Abatement Strategy)
12
were mapped into GTAP. The 5 USEPA NCGG emissions categories and 34
subcategories not currently mapped into GTAP include:
a. Specific biomass burning N2O and CH4 emissions not uniquely
attributable to anthropogenic activity (middle and high latitude forest fires,
middle and high latitude grassland fires, indirect N2O from tropical forest
fires, tropical forest fires).
b. Biomass burning tropical forest fire deforestation N2O, CH4, and CO2
emissions. Currently omitted because the emissions are associated with
land-use change, and the GTAP land-use database (Lee et al., 2005) does
not provide land-use change data. Please note however that GTAP forest
carbon stock data is available that is consistent with the GTAP forest
inventory dataset (see the previous chapter6). This data will allow for
modeling changes of forest carbon.
c. Biomass combustion N2O, CH4, and CO2 emissions. Omitted from
mapping because the GTAP energy database does not currently include
biomass energy volumes.
d. Methane from underground storage and geothermal energy. Only one
country reported emissions in each of these subcategories, and the
emissions were modest: Latvia (underground storage emissions of 0.33
Gg), and New Zealand (geothermal emissions of 2.47 Gg).
e. Other CO2 emissions not attributable to fossil fuel combustion. This
includes fugitive and combustion CO2 emissions from the chemical
6 GTAP Working Paper No. 42
13
industry and metal production, fugitive CO2 emissions from oil
production/transmission/handling, and CO2 emissions associated with
cement production. The first of these three categories was omitted due to
concerns about double counting with the GTAP CO2 combustion
emissions database. The second and third will be added to the GTAP
emissions database in the future.
Overall, the omitted emissions subcategories will be added to the database in the future as
methodologies are developed and activity data becomes available. The USEPA emissions
data omitted from the GTAP mapping are described in Rose et al. (2007c) and can be
obtained from USEPA (Rose et al., 2007b).
Each of the USEPA emissions subcategories was individually mapped to the
GTAP version 6 database’s region and sector structure (87 regions, 57 sectors), and
regional households (Table 3). For each of the USEPA emissions subcategories, the
relevant set of emitting GTAP sectors was identified from a careful matching of IPCC
emissions source definitions and driver descriptions (IPCC, 1997a, 1997b, 2000, 2003) to
the underlying United Nations Central Product Classification (CPC) and International
Standard Industrial Classification (ISIC) definitions associated with the GTAP sectors
(see Appendix A for the CPC and ISIC codes associated with the GTAP sectors).
Many USEPA emissions subcategories mapped directly to individual GTAP
sectors for each country (Table 3). However, disaggregation methodologies were required
for subcategories that mapped to multiple GTAP sectors and/or when there were multiple
emitting activities (e.g,, CH4 and N2O emissions from combustion of coal, natural gas,
and oil in GTAP energy sectors col, oil, gas, p_c, ely, and gdt). Where possible, GTAP
14
input activity data was exploited for subcategory emissions disaggregation across sectors
in order to integrate the datasets, thereby providing greater consistency across datasets.
There were four cases where an USEPA emissions category/subcategory did not
map directly to a GTAP sector or country. In each case, shares were developed, either
sector shares or country shares.
• Case 1: Category/subcategory maps to multiple GTAP sectors and there is
only one emitting activity
• Case 2: Category/subcategory maps to multiple GTAP sectors and there are
multiple emitting activities
• Case 3: Category/subcategory maps to multiple GTAP sectors but emissions
source is poorly defined – this case applies only to livestock related
subcategory designations of “UNKNOWN.”
• Case 4: Category/subcategory includes aggregated regional emissions for a
few smaller emitting countries that could not be disaggregated – this case
applies only to two USEPA emissions categories—agricultural soils and
pasture, range, and paddock.
For cases 1 and 2, GTAP base year activity and IPCC emissions factor data are applied
when available. If not available, other methods were employed, such as using GTAP
production shares. See Rose et al. (2007c) for a complete description of the mapping
methodologies and the specific mapping and disaggregation handling for each
subcategory.
15
Table 3. Mapping NCGG categories and subcategories to GTAP v6 sectors and emissions drivers
Category Subcategory GHG GTAP sector Emissions driver(s)
Adipic and Nitric Production Adipic Acid Production N2O crp Output
Nitric Acid Production N2O crp Output
Agricultural SoilsCrop soils only - pasture, range, paddock disaggregated into its
own category (below)N2O pdr, wht, gro, v_f,
osd, c_b, pfb, ocr Input (crp)
Biomass Burning Agricultural Waste Burning CH4 & N2O
pdr, wht, gro, v_f, osd, c_b, pfb, ocr Output
Savannah and Shrubs Fires CH4 & N2O ctl Endowment (land)
Fugitives from Coal Mining Activities CH4 col Output
Fugitives from Oil and Natural Gas
Systems Natural gas - distribution CH4 gdt Output
Natural gas - exploration CH4 gas Output Natural Gas - flaring CH4 gas Output
Natural gas - leakage CH4 gdt Output Natural gas - leakage at
industrial plants and power stations
CH4 gdt Output
Natural gas - leakage at residential and commercial
sectorsCH4 gdt Output
Natural gas - production/processing CH4 gas, gdt Output
Natural gas - transmission CH4 otp Output Natural Gas - venting CH4 gas Output
Oil - distribution of products CH4 p_c Output Oil - exploration CH4 oil Output
Oil - flaring CH4 oil Output Oil - other CH4 oil Output
Oil - production CH4 oil Output Oil - refining and storage CH4 p_c Output
Oil - transport CH4 otp Output Oil - venting CH4 oil Output
Human Sewage N2O osg OutputLandfilling of Solid
Waste CH4 osg Output
Livestock Enteric Fermentation BUFFALO CH4 ctl Endowment (capital)
CAMEL (includes reportings for camels, alpaca, llamas, and
camelids)CH4 ctl Endowment (capital)
DAIRY_CATTLE CH4 rmk Endowment (capital)GOAT CH4 ctl Endowment (capital)
HORSE CH4 ctl Endowment (capital)MULE/ASS CH4 ctl Endowment (capital)
NON-DAIRY_CATTLE (includes reportings for non-dairy cattle,
deer, and reindeer)CH4 ctl Endowment (capital)
OTHER (includes reportings for fur bearing animals, ostrich, emus, rabbits, and "other")
CH4 oap Endowment (capital)
POULTRY (includes reportings for chickens, ducks, geese,
turkeys, and "poultry")CH4 oap Endowment (capital)
SHEEP/LAMB CH4 ctl Endowment (capital)SWINE CH4 oap Endowment (capital)
UNKNOWN (not specified in reporting) CH4 ctl, oap, rmk Endowment (capital)
16
Category Subcategory GHG GTAP sector Emissions driver(s)
Livestock Manure Management BUFFALO CH4 &
N2O ctl Endowment (capital)
CAMEL (includes reportings for camels and camelids)
CH4 & N2O ctl Endowment (capital)
DAIRY_CATTLE CH4 & N2O rmk Endowment (capital)
GOAT CH4 & N2O ctl Endowment (capital)
HORSE (includes reportings for horses and combined reportings
that include horses/goats/asses/mules/rabbit
s(
CH4 & N2O ctl Endowment (capital)
MULE/ASS CH4 & N2O ctl Endowment (capital)
NON-DAIRY_CATTLE (includes reportings for non-dairy cattle, 1 to 3 year cattle, fat calves. deer,
and equidea)
CH4 & N2O ctl Endowment (capital)
OTHER (includes reportings for fur bearing animals and rabbits) N2O oap Endowment (capital)
POULTRY (Includes reportings for chickens, boilers, hens, ducks, geese, turkeys, and
"poultry")
CH4 & N2O oap Endowment (capital)
SHEEP/LAMB CH4 & N2O ctl Endowment (capital)
SWINE (includes reportings for swine, pig, and sow)
CH4 & N2O oap Endowment (capital)
UNKNOWN (not specified in reporting)
CH4 & N2O ctl, oap, rmk Endowment (capital)
Other Industrial Non-Agricultural Sources Mineral production CH4 nmm Output
Chemical production CH4 crp OutputChemical production N2O crp Output
Iron, steel, & ferroalloys production CH4 i_s Output
Iron, steel, & ferroalloys production N2O i_s Output
Aluminum & non-ferrous Production CH4 nfm Output
All metal production CH4 i_s, nfm OutputOther CH4 omf, ppp OutputOther N2O omf, ppp Output
Pasture, Range, and Paddock BUFFALO N2O ctl Endowment (capital)
DAIRY_CATTLE N2O rmk Endowment (capital)
GOAT (includes reportings for goats and combined reportings
that include goats/horses/deer/buffalo/donkeys/mules/emus/alpaca/camels)
N2O ctl Endowment (capital)
HORSE (includes reportings for horses and combined reportings
that include horses/goats/asses/mules/rabbit
s)
N2O ctl Endowment (capital)
MULE/ASS N2O ctl Endowment (capital)NON-DAIRY_CATTLE (includes reportings for non-dairy cattle, 1 to 3 year cattle, fat calves. deer,
and equidea)
N2O ctl Endowment (capital)
OTHER (includes reportings for fur bearing animals and rabbits) N2O oap Endowment (capital)
POULTRY N2O oap Endowment (capital)SHEEP/LAMB N2O ctl Endowment (capital)
17
3.3 Mapping to GTAP emissions drivers
Tying emissions as closely as possible to emissions drivers allows for a more
refined representation of abatement technologies and responses. For instance, there are
many NCGG emissions that are closely related to input use. Nitrous oxide emissions
from fertilizer usage and methane emissions from livestock are two obvious examples.
With emissions tied to particular inputs, inputs can be adjusted to manage emissions
while production is maintained via input substitution. When it is difficult to tie emissions
Category Subcategory GHG GTAP sector Emissions driver(s)
Rice Cultivation CH4 pdr Endowment (land)
Stationary and Mobile Combustion
Stationary Combustion: Energy Industries
CH4 & N2O
col, oil, gas, p_c, ely, gdt
Inputs (refined oil (ep_c), coal (ecol), natural gas (egdt))
Stationary Combustion: Total Industry Sector
CH4 & N2O
omn, cmt, omt, vol, mil, pcr, sgr, ofd, b_t, tex, wap, lea,
lum, ppp, crp, nmm, i_s, nfm, fmp, mvh, otn, ele, ome,
omf, cns
Inputs (refined oil (ep_c), coal (ecol), natural gas (egdt))
Mobile Combustion: Total Transport Sector
CH4 & N2O otp, wtp, atp Inputs (refined oil (ep_c), coal
(ecol), natural gas (egdt))
Stationary and Mobile Combustion: Agriculture
CH4 & N2O
Crop sectors 1-8, livestock sectors 9-12, forestry, fishing
Inputs (refined oil (ep_c), coal (ecol), natural gas (egdt))
Stationary Combustion: Commercial and Public Services
CH4 & N2O
wtr, trd, cmn, ofi, isr, obs, ros, osg
Inputs (refined oil (ep_c), coal (ecol), natural gas (egdt))
Stationary Combustion: Residential
CH4 & N2O households Inputs (refined oil (ep_c), coal
(ecol), natural gas (egdt))
Non-specified Other CH4 & N2O osg Inputs (refined oil (ep_c), coal
(ecol), natural gas (egdt))Wastewater Treatment CH4 osg Output
Aluminum Production Aluminum Production CF4 nfm Output
Electrical Transmission and
Distribution
Electrical Transmission and Distribution SF6 ely Output
HCFC-22 Production HCFC-22 Production HFC-23 crp Output
Magnesium Manufacturing Magnesium Manufacturing SF6 nfm Output
ODS Substitutes Aerosols (MDI) HFC-134a crp OutputAerosols (Non-MDI) HFC-134a crp OutputFire Extinguishing HFC-134a crp Output
Foams HFC-134a crp OutputRefrigeration/AC HFC-134a ele Output
Solvents HFC-134a crp OutputSemiconductor
Production Semiconductor Production CF4 ele Output
18
directly to input usage due to a lack of (a) input use data, (b) scientific understanding of
emissions generation processes, or (c) econometric production cost estimates, emissions
are tied to the aggregate output of the sector.
The detailed specification of the GTAP endowment, intermediate input (“Input”),
or output driver for each subcategory is listed in the last column of Table 3. In most
cases, all the emissions associated with a category were assigned to the same type of
driver. For biomass burning emissions, the specific subcategories were assigned unique
drivers. For stationary and mobile combustion emissions, emissions were disaggregated
and tied to each of the fossil fuel combustion activities.
It is important for modelers to recognize that specific emissions generation
processes are obscured by these aggregated emissions-driver relationships. For instance,
manure emissions depend on, among other things, the number of animals and the manure
management system. Variation in either element of production across regions is
represented by differences in capital in the GTAP database. Base year regional
differences in the combination of animal number and manure management will be
captured in the relationship between emissions and capital. However, the relationship will
change over time due to autonomous and policy-driven technological change. Modelers
need to be mindful of dynamics in the emissions-driver relationships to avoid unrealistic
growth in future emissions and to appropriately apply mitigation technologies. See Hertel
et al. (2006) and chapter 67 in this volume, which utilize the GTAP NCGG emissions
database and USEPA (2006b) mitigation cost data, to develop and apply an initial
detailed NCGG mitigation modeling framework specifically for agricultural activities.
7 GTAP Working Paper No. 44
19
4. NCGG Data Overview
This section provides a graphical overview of the GTAP NCGG emissions
database. Below are a variety of figures that were selected to give the reader a feel for the
structure of the emissions data and level of disaggregation. The first set of figures
illustrates global NCGG emissions by sector, region, and gas (Figures 2-3). Figure 2
illustrates that by far the largest NCGG emitting economic activity globally is the
production of ruminant livestock (e.g., non-dairy cattle, sheep, goats, and horses) which
generates enteric methane emissions as well as manure methane and nitrous oxide
emissions. The next largest emitting activity is the provision of public services, where
methane and nitrous oxide emissions are generated from wastewater, human sewage, and
landfill activities, as well as stationary fossil fuel combustion processes.
Figure 3 identifies the top NCGG emitting regions: China (“chn”), the United
States (“usa”), India (“ind”), and Brazil (“bra”). As was true for sectors (Figure 2), the
distribution of gases across regions varies significantly. Noticeably, F-gases are a
relatively small part of the global carbon equivalent emissions and are concentrated in the
relatively few countries responsible for the vast majority of electronics, metals, and
chemicals production.
The second set of figures (Figures 4 and 5) delve deeper into the data, presenting
the NCGG subcategory emissions for two illustrative regions—the United States and
China. Here we see that the data suggests that NCGG emissions come from a larger set of
sectors in the US economy than in the Chinese economy; the F-gases are much more
prominent proportionally in the US economy (electronic equipment manufacturing in
particular); while paddy rice, ruminant livestock, non-ruminant livestock, and coal
20
production are more dominant emitting activities in the Chinese economy. Waste
handling (wastewater, human sewage, and landfills) is a large NCGG emissions source in
both economies. Fugitive CH4 and stationary and mobile combustion CH4 and N2O
emissions, as well as dairy cattle CH4 and N2O emissions are noticeable in the US data,
and almost non-existent in the Chinese data.
Figure 6 illustrates an additional dimension of the dataset that ties sector-level
emissions to emissions drivers. Specifically, Figure 6 presents the USA NCGG emissions
by sector in terms of emissions driver groups—endowments, intermediate inputs, and
output. For instance, the “otp” sector includes both land transportation as well as pipeline
transmission activities. NCGG fossil fuel combustion related emissions are attributed to
output, while fugitive methane emissions occurring during transmission of fuels over
pipelines is associated to fuel input levels. In land related economic sectors, NCGG
emissions are mapped primarily to inputs, such as intermediate inputs like fertilizer use,
and endowments like livestock capital stock and acreage. To simplify Figure 6, the
subcategory emissions in each sector were aggregated by emissions driver.
5. Conclusion NCGG emissions are important factors in climate change and should be
considered for proper evaluation of the net environmental effectiveness of climate change
policies. Furthermore, NCGGs mitigation technologies can add “what” flexilibility to
“when” and “where” mitigation flexibility in achieving climate change goals. As a result,
analysts will want to consider the potential emissions and mitigation impacts of NCGGs
in the design of cost-effective policies. The disaggregated globally consistent NCGG
21
dataset presented in this chapter was designed to facilitate more sophisticated assessment
of the climate change role and mitigation opportunities associated with NCGGs. With
greater country and emissions source resolution, the data was directly integrated with
economic activity and specific emissions drivers; thereby, providing a better
characterization of differences in sectoral and regional NCGG profiles and allowing for
more refined evaluation of heterogeneous regional and sectoral production and
consumption responses.
22
Figure 2. 2001 global NCGG emissions by sector and gas (MtCeq)
0 100 200 300 400 500 600 700
1 pdr2 wht3 gro4 v_f5 osd6 c_b7 pfb8 ocr9 ctl
10 oap11 rmk12 wol13 frs14 fsh15 coa
16 oil17 gas
18 omn19 cmt20 omt21 vol22 mil23 pcr24 sgr25 ofd26 b_t27 tex
28 wap29 lea
30 lum31 ppp32 p_c33 crp
3435 i_s
36 nfm37 fmp38 mvh39 otn40 ele
41 ome42 omf43 ely44 gdt45 wtr46 cns47 trd48 otp49 wtp50 atp
51 cmn52 ofi53 isr
54 obs55 ros56 osg57 dwe
HH
MtCeq
CH4
N2O
Fgases
23
Figure 3. 2001 global NCGG emissions by region and gas (MtCeq)
0 50 100 150 200 250 300 350 400
1 aus2 nzl3 xoc4 chn5 hkg6 jpn7 kor8 twn9 xea
10 idn11 mys12 phl
13 sgp14 tha
15 vnm16 xse17 bgd18 ind19 lka20 xsa21 can22 usa23 mex24 xna25 col26 per27 ven28 xap29 arg30 bra31 chl32 ury
33 xsm34 xca35 xfa36 xcb37 aut38 bel
39 dnk40 fin41 fra
42 deu43 gbr44 grc
45 irl46 ita47 lux48 nld49 prt
50 esp51 swe52 che53 xef54 xer55 alb56 bgr57 hrv58 cyp59 cze60 hun61 mlt62 pol
63 rom64 svk65 svn66 est67 lva68 ltu
69 rus70 xsu71 tur
72 xme73 mar74 tun75 xnf
76 bwa77 zaf78 xsc79 mwi80 moz81 tza
82 zmb83 zwe84 xsd
85 mdg86 uga87 xss
MtCeq
CH4
N2O
Fgases
24
Figure 4. 2001 United States NCGG emissions by sector and source (MtCeq)
0 10 20 30 40 50 60
1 pdr2 wht3 gro4 v_f
5 osd6 c_b7 pfb8 ocr9 ctl
10 oap11 rmk12 wol13 frs14 fsh15 coa
16 oil17 gas
18 omn19 cmt20 omt21 vol22 mil23 pcr24 sgr25 ofd26 b_t27 tex
28 wap29 lea30 lum31 ppp32 p_c33 crp
3435 i_s
36 nfm37 fmp38 mvh39 otn40 ele
41 ome42 omf43 ely44 gdt45 wtr46 cns47 trd48 otp49 wtp50 atp
51 cmn52 ofi53 isr
54 obs55 ros56 osg57 dwe58 HH
MtCeq
Biomass burning CH4
Coal CH4
Enteric fermentation CH4
Other industrial non-ag CH4
Landfill CH4
Manure management CH4
Oil & gas fugitives CH4
Rice cultivation CH4
Stationary & mobile combustion CH4
Wastewater CH4
Adipic & nitric acid N2O
Biomass burning N2O
Human sewage N2O
Other industrial non-ag N2O
Manure management N2O
Pasture, range, paddock N2O
Stationary & mobile combustion N2O
Ag soils N2O
Aluminum production CF4
Electrical trans. & distr. SF6
HCFC-22 production HFC-23
Magnesium manufacturing SF6
ODS substitutes HFC-134a
Semiconductor production CF4
25
Figure 5. 2001 China NCGG emissions by sector and source (MtCeq)
0 10 20 30 40 50 60 70 80
1 pdr2 wht3 gro4 v_f
5 osd6 c_b7 pfb8 ocr9 ctl
10 oap11 rmk12 wol13 frs14 fsh15 coa
16 oil17 gas
18 omn19 cmt20 omt21 vol22 mil23 pcr24 sgr25 ofd26 b_t27 tex
28 wap29 lea
30 lum31 ppp32 p_c33 crp
34 nmm35 i_s
36 nfm37 fmp38 mvh39 otn40 ele
41 ome42 omf43 ely44 gdt45 wtr46 cns47 trd48 otp49 wtp50 atp
51 cmn52 ofi53 isr
54 obs55 ros56 osg57 dwe58 HH
MtCeq
Biomass burning CH4
Coal CH4
Enteric fermentation CH4
Other industrial non-ag CH4
Landfill CH4
Manure management CH4
Oil & gas fugitives CH4
Rice cultivation CH4
Stationary & mobile combustion CH4
Wastewater CH4
Adipic & nitric acid N2O
Biomass burning N2O
Human sewage N2O
Other industrial non-ag N2O
Manure management N2O
Pasture, range, paddock N2O
Stationary & mobile combustion N2O
Ag soils N2O
Aluminum production CF4
Electrical trans. & distr. SF6
HCFC-22 production HFC-23
Magnesium manufacturing SF6
ODS substitutes HFC-134a
Semiconductor production CF4
26
Figure 6. 2001 United States NCGG emissions by sector and emissions driver type (MtCeq)
0 10 20 30 40 50 60
1 pdr2 wht3 gro4 v_f5 osd6 c_b7 pfb8 ocr9 ctl
10 oap11 rmk12 wol13 frs14 fsh15 coa
16 oil17 gas
1819 cmt20 omt21 vol22 mil23 pcr24 sgr25 ofd26 b_t27 tex
28 wap29 lea
30 lum31 ppp32 p_c33 crp
3435 i_s
36 nfm37 fmp38 mvh39 otn40 ele
41 ome42 omf43 ely44 gdt45 wtr46 cns47 trd48 otp49 wtp50 atp
51 cmn52 ofi53 isr
54 obs55 ros56 osg57 dwe
MtCeq
Output N2O
Endowments N2O
Intermediate inputs N2O
Output CH4
Endowments CH4
Intermediate inputs CH4
Output F-gas
27
6. References
de la Chesnaye, F.C. and J.P Weyant, (eds.), 2006: Multigas Mitigation and Climate Policy. The Energy Journal Special Issue #3.
Hertel, T., H-L. Lee, S. Rose, and B. Sohngen, 2006. “The Role of Global Land Use in
Determining Greenhouse Gases Mitigation Costs”. GTAP Working Paper No. 36, December 2006, https://www.gtap.agecon.purdue.edu.
Hourcade, J.-C., Shukla, P.R., Cifuentes, L., Davis, D., Emonds, J., Fisher, B., Fortin, E.,
Golub, A., Hohmeyer, O., Krupnick, A., Kverndokk, S., Loulou, R., Richels, R., Segenovic, H., Yamaji, K., (2001). “Global, Regional, and National Costs and Ancillary Benefits of Mitigation,” Chapter 8 in Climate Change 2001: Mitigation — Contribution of Working Group III to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, pp. 702.
IPCC. 1996. Climate Change 1995: The Science of Climate Change. Intergovernmental
Panel on Climate Change. Edited by J.T. Houghton, L.G. Meira Filho, B.A. Callender, N. Harris, A. Kattenberg, and K. Maskell. Cambridge, UK: Cambridge University Press.
IPCC, 1997a. Revised 1996 Guidelines for National Greenhouse Gas Inventories –
Volume 1: Reporting Instructions, Intergovernmental Panel on Climate Change. IPCC, 1997b. Revised 1996 Guidelines for National Greenhouse Gas Inventories –
Volume 3: Reference Manual, Intergovernmental Panel on Climate Change. IPCC, 2000. Good Practice Guidance and Uncertainty Management in National
Greenhouse Gas Inventories, Intergovernmental Panel on Climate Change. IPCC. 2001. Climate Change 2001: The Scientific Basis, Intergovernmental Panel on
Climate Change. Edited by J.T. Houghton, Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, C.A. Johnson, and K. Maskell. Cambridge, UK: Cambridge University Press. Available online at <http://www.grida.no/climate/ipcc_tar/wg1/519.htm>.
IPCC, 2003. Good Practice Guidance for Land Use, Land-Use Change and Forestry,
Intergovernmental Panel on Climate Change. Lee, 2005. “An Emissions Data Base for Integrated Assessment of Climate Change
Policy Using GTAP” GTAP Resource #1143, Center for Global Trade Analysis, Purdue University, https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=1143
28
Olivier, J.G.J., 2002. Part III: Greenhouse gas emissions: 1. Shares and trends in greenhouse gas emissions; 2. Sources and Methods; Greenhouse gas emissions for 1990 and 1995. In: "CO2 emissions from fuel combustion 1971-2000", 2002 Edition, pp. III.1-III.31. International Energy Agency (IEA), Paris. ISBN 92-64-09794-5.
Rose, S., H. Ahammad, B. Eickhout, B. Fisher, A. Kurosawa, S. Rao, K. Riahi, and D. van
Vuuren, 2007a. “Land in climate stabilization modeling,” Energy Modeling Forum Report, Stanford University, http://www.stanford.edu/group/EMF/home/index.htm
Rose, S., S. Finn, E. Scheele, J. Mangino, K. Delhotal, J. Siedenburg, H. Perez, 2007b.
“Detailed greenhouse gas emissions data for global economic modeling”, United States Environmental Protection Agency, Washington, DC.
Rose, S., Lee, H.-L., T. Hertel, M. Avetisyan, 2007c. A Greenhouse Gases Data Base for
Analysis of Climate Change Mitigation. Draft GTAP Technical Paper, Center for Global Trade Analysis, Purdue University.
USEPA, 2006a. Global Emissions of Non- CO2 Greenhouse Gases: 1990-2020. United
States Environmental Protection Agency (US-EPA), Washington, D.C., EPA Report 430-R-06-003, http://www.epa.gov/nonco2/econ-inv/international.html
USEPA, 2006b: Global Mitigation of Non- CO2 Greenhouse Gases, United States
Environmental Protection Agency, Washington, DC, EPA Report 430-R-06-005, http://www.epa.gov/nonco2/econ-inv/international.html
29
Appendix A. GTAP sectoral classification Source : GTAP database version 6 documentation (https://www.gtap.agecon.purdue.edu/default.asp) Tables A1 and A2 below show the sectoral definitions used in version 6.0 of the GTAP data base. The GTAP agricultural and food processing sectors are defined by reference to the Central Product Classification (CPC), as shown in table A1. The other GTAP sectors are defined by reference to the International Standard Industry Classification (ISIC), as shown in table A2. The ISIC is used for most sectors, because it is the reference point for sectoral classification in most I-O statistics. But for agriculture and food processing, the ISIC does not provide the detail GTAP needs, so CPC is used instead. The CPC was developed by the Statistical Office of the United Nations to serve as a bridge between the ISIC and other sectoral classifications (UN 1990, 1991).
Table A1. GSC2 Sectors defined by Reference to the Provisional CPC GSC2 Number
Code CPC Code
Description
1
pdr
0113 0114
Rice, not husked Husked rice
2 wht 0111 Wheat and meslin 3
gro
0112 0115 0116 0119
Maize (corn) Barley Rye, oats Other cereals
4
v_f
012 013
Vegetables Fruit and nuts
5 osd 014 Oil seeds and oleaginous fruit 6 c_b 018 Plants used for sugar manufacturing 7 pfb 0192 Raw vegetable materials used in textiles 8
ocr
015 016 017 0191
0193 0194 0199
Live plants; cut flowers and flower buds; flower seeds and fruit seeds; vegetable seeds Beverage and spice crops Unmanufactured tobacco Cereal straw and husks, unprepared, whether or not chopped, ground, pressed or in the form of pellets; swedes, mangolds, fodder roots, hay, lucerne (alfalfa), clover, sainfoin, forage kale, lupines, vetches and similar forage products, whether or not in the form of pellets Plants and parts of plants used primarily in perfumery, in pharmacy, or for insecticidal, fungicidal or similar purposes Sugar beet seed and seeds of forage plants Other raw vegetable materials
9
ctl
0211 0299
Bovine cattle, sheep and goats, horses, asses, mules, and hinnies, live Bovine semen
Contd
30
GSC2 Number
Code CPC Code
Description
10
oap
0212 0292 0293 0294 0295 0297 0298
Swine, poultry and other animals, live Eggs, in shell, fresh, preserved or cooked Natural honey Snails, live, fresh, chilled, frozen, dried, salted or in brine, except sea snails; frogs’ legs, fresh, chilled or frozen Edible products of animal origin n.e.c. Hides, skins and furskins, raw Insect waxes and spermaceti, whether or not refined or coloured
11 rmk 0291 Raw milk 12 wol 0296 Raw animal materials used in textile
13 for 03 Forestry, logging and related service activities 19
cmt
21111 21112 21115 21116 21117 21118 21119 2161
Meat of bovine animals, fresh or chilled Meat of bovine animals, frozen Meat of sheep, fresh or chilled Meat of sheep, frozen Meat of goats, fresh, chilled or frozen Meat of horses, asses, mules or hinnies, fresh, chilled or frozen Edible offal of bovine animals, swine, sheep, goats, horses, asses, mules or hinnies, fresh, chilled or frozen Fats of bovine animals, sheep, goats, pigs and poultry, raw or rendered; wool grease
20
omt
21113 21114 2112 2113 2114 2162
Meat of swine, fresh or chilled Meat of swine, frozen Meat and edible offal, fresh, chilled or frozen, n.e.c. Preserves and preparations of meat, meat offal or blood Flours, meals and pellets of meat or meat offal, inedible; greaves Animal oils and fats, crude and refined, except fats of bovine animals, sheep, goats, pigs and poultry
21 vol 2163 2164 2165
2166 2167
Soya-bean, ground-nut, olive, sunflower-seed, safflower, cotton-seed rape, colza and mustard oil, crude Palm, coconut, palm kernel, babassu and linseed oil, crude Soya-bean, ground-nut, olive, sunflower-seed, safflower, cotton-seed, rape, colza and mustard oil and their fractions, refined but not chemically modified; other oils obtained solely from olives and sesame oil, and their fractions, whether or not refined, but not chemically modified Maize (corn) oil and its fractions, not chemically modified Palm, coconut, palm kernel, babassu and linseed oil and their fractions, refined but not chemically modified; castor, tung and jojoba oil and fixed vegetable fats and oils (except maize oil) and their fractions n.e.c., whether or not refined, but not chemically modified
31
Table A1. GSC2 Sectors defined by Reference to the Provisional CPC (Continued) GSC2 Number
Code CPC Code
Description
21 vol 2168 2169
217 218
Margarine and similar preparations Animal or vegetable fats and oils and their fractions, partly or wholly hydrogenated, inter-esterified, re-esterified or elaidinised, whether or not refined, but not further prepared Cotton linters Oil-cake and other solid residues resulting from the extraction of vegetable fats or oils; flours and meals of oil seeds or oleaginous fruits, except those of mustard; vegetable waxes, except triglycerides; degras; residues resulting from the treatment of fatty substances or animal or vegetable waxes
22 mil 22 Dairy products 23 pcr 2316 Rice, semi- or wholly milled 24 sgr 235 Sugar 25
ofd
212 213 214 215 2311 2312 2313 2314 2315 2317 2318 232 233 234 236 237 239
Prepared and preserved fish Prepared and preserved vegetables Fruit juices and vegetable juices Prepared and preserved fruit and nuts Wheat or meslin flour Cereal flours other than of wheat or meslin Groats, meal and pellets of wheat Cereal groats, meal and pellets n.e.c. Other cereal grain products (including corn flakes) Other vegetable flours and meals Mixes and doughs for the preparation of bakers’ wares Starches and starch products; sugars and sugar syrups n.e.c. Preparations used in animal feeding Bakery products Cocoa, chocolate and sugar confectionery Macaroni, noodles, couscous and similar farinaceous products Food products n.e.c.
26
b_t
24 25
Beverages Tobacco products
n.e.c. Not elsewhere classified
32
Table A2. GSC2 Sectors defined by Reference to the ISIC, Rev. 3 GSC2 Number
Code ISIC3 Code
Description
14 fsh
015 05
Hunting, trapping and game propagation including related service activities Fishing, operation of fish hatcheries and fish farms; service activities incidental to fishing
15
col
101 102
Mining and agglomeration of hard coal Mining and agglomeration of lignite
16
oil
111 112 103
Extraction of crude petroleum and natural gas (part) Service activities incidental to oil and gas extraction excluding surveying (part) Mining and agglomeration of peat
17
gas
111 112
Extraction of crude petroleum and natural gas (part) Service activities incidental to oil and gas extraction excluding surveying (part)
18
omn
12 13 14
Mining of uranium and thorium ores Mining of metal ores Other mining and quarrying
27
tex
17 243
Manufacture of textiles Manufacture of man-made fibres
28 wap 18 Manufacture of wearing apparel; dressing and dyeing of fur 29 lea 19 Tanning and dressing of leather; manufacture of luggage, handbags,
saddlery, harness and footwear 30 lum 20 Manufacture of wood and of products of wood and cork, except furniture;
manufacture of articles of straw and plaiting materials 31
ppp
21 2211 2212 2213 2219 222 223
Manufacture of paper and paper products Publishing of books, brochures, musical books and other publications Publishing of newspapers, journals and periodicals Publishing of recorded media Other publishing (photos, engravings, postcards, timetables, forms, posters, art reproductions, etc.) Printing and service activities related to printing Reproduction of recorded media
32
p_c
231 232 233
Manufacture of coke oven products Manufacture of refined petroleum products Processing of nuclear fuel
33
crp
241 242 25
Manufacture of basic chemicals Manufacture of other chemical products Manufacture of rubber and plastics products
34 nmm 26 Manufacture of other non-metallic mineral products35
i_s
271 2731
Manufacture of basic iron and steel Casting of iron and steel
33
Table A2. GSC2 Sectors defined by Reference to the ISIC, Rev. 3 (Continued) GSC2 Number
Code ISIC3 Code
Description
36
nfm
272 2732
Manufacture of basic precious and non-ferrous metals Casting of non-ferrous metals
37 fmp 28 Manufacture of fabricated metal products, except machinery and equipment
38 mvh 34 Manufacture of motor vehicles, trailers and semi-trailers39 otn 35 Manufacture of other transport equipment40
ele
30 32
Manufacture of office, accounting and computing machinery Manufacture of radio, television and communication equipment and apparatus
41
ome
29 31 33
Manufacture of machinery and equipment n.e.c. Manufacture of electrical machinery and apparatus n.e.c. Manufacture of medical, precision and optical instruments, watches and clocks
42
omf
36 37
Manufacturing n.e.c. Recycling
43 ely 401 Production, collection and distribution of electricity44
gdt
402 403
Manufacture of gas; distribution of gaseous fuels through mains Steam and hot water supply
45 wtr 41 Collection, purification and distribution of water46 cns 45 Construction47
trd
50 51 521 522 523 524 525 526 55
Sales, maintenance and repair of motor vehicles and motorcycles; retail sale of automotive fuel Wholesale trade and commission trade, except of motor vehicles and motorcycles Non-specialized retail trade in stores Retail sale of food, beverages and tobacco in specialized stores Other retail trade of new goods in specialized stores Retail sale of second-hand goods in stores Retail trade not in stores Repair of personal and household goods Hotels and restaurants
48 otp
60 63
Land transport; transport via pipelines Supporting and auxiliary transport activities; activities of travel agencies
49 wtp 61 Water transport50 atp 62 Air transport51 cmn 64 Post and telecommunications52
ofi
65 67
Financial intermediation, except insurance and pension funding Activities auxiliary to financial intermediation
53 isr 66 Insurance and pension funding, except compulsory social security Contd
34
Table A2. GSC2 Sectors defined by Reference to the ISIC, Rev. 3 (Continued) GSC2 Number
Code ISIC3 Code
Description
54
obs
70 711 712 713 72 73 74
Real estate activities Renting of transport equipment Renting of other machinery and equipment Renting of personal and household goods n.e.c. Computer and related activities Research and development Other business activities
55
ros
92 93 95
Recreational, cultural and sporting activities Other service activities Private households with employed persons
56
osg
75 80 85 90 91 99
Public administration and defence; compulsory social security Education Health and social work Sewage and refuse disposal, sanitation and similar activities Activities of membership organizations n.e.c. Extra-territorial organizations and bodies
57 dwe n.a. n.a. n.a. Not available n.e.c. Not elswhere classified References United Nations. 1990. International Standard Industrial Classification of All Economic Activities, Third
Revision, Statistical Paper Series M No. 4, Rev. 3, Sales No. E.91.XVII.7. New York: United Nations Publishing Division.
United Nations. 1991. Provisional Central Product Classification, Statistical Paper Series M No. 77, Sales No. E.91.XVII.7. New York: United Nations Publishing
35
Appendix B. Regions in the GTAP 6 Data Base and Mapping to Standard Countries Number
Code Name Member Regions (226) Code
1 AUS Australia Australia AUS 2 NZL New Zealand New Zealand NZL 3 XOC Rest of Oceania American Samoa ASM Cook Islands COK Fiji FJI French Polynesia PYF Guam GUM Kiribati KIR Marshall Islands MHL Micronesia, Federated States of FSM Nauru NRU New Caledonia NCL Norfolk Island NFK Northern Mariana Islands MNP Niue NIU Palau PLW Papua New Guinea PNG Samoa WSM Solomon Islands SLB Tokelau TKL Tonga TON Tuvalu TUV Vanuatu VUT Wallis and Futuna WLF 4 CHN China China CHN 5 HKG Hong Kong Hong Kong HKG 6 JPN Japan Japan JPN 7 KOR Korea Korea, Republic of KOR 8 TWN Taiwan Taiwan TWN 9 XEA Rest of East Asia Macau MAC Mongolia MNG Korea, Democratic People’s
Republic of PRK
10 IDN Indonesia Indonesia IDN 11 MYS Malaysia Malaysia MYS 12 PHL Philippines Philippines PHL 13 SGP Singapore Singapore SGP 14 THA Thailand Thailand THA 15 VNM Viet Nam Viet Nam VNM
36
16 XSE Rest of Southeast Asia Brunei Darussalam BRN Cambodia KHM Lao People’s Democratic
Republic LAO
Myanmar MMR Timor Leste TLS 17 BGD Bangladesh Bangladesh BGD 18 IND India India IND 19 LKA Sri Lanka Sri Lanka LKA 20 XSA Rest of South Asia Afghanistan AFG Bhutan BTN Maldives MDV Nepal NPL Pakistan PAK 21 CAN Canada Canada CAN 22 USA United States of America United States of America USA 23 MEX Mexico Mexico MEX 24 XNA Rest of North America Bermuda BMU Greenland GRL Saint Pierre and Miquelon SPM 25 COL Colombia Colombia COL 26 PER Peru Peru PER 27 VEN Venezuela Venezuela VEN 28 XAP Rest of Andean Pact Bolivia BOL Ecuador ECU 29 ARG Argentina Argentina ARG 30 BRA Brazil Brazil BRA 31 CHL Chile Chile CHL 32 URY Uruguay Uruguay URY 33 XSM Rest of South America Falkland Islands (Malvinas) FLK French Guiana GUF Guyana GUY Paraguay PRY Suriname SUR 34 XCA Central America Belize BLZ Costa Rica CRI El Salvador SLV Guatemala GTM Honduras HND Nicaragua NIC Panama PAN 35 XFA Rest of Free Trade Area of the
Americas Antigua & Barbuda ATG
37
Bahamas BHS Barbados BRB Dominica DMA Dominican Republic DOM Grenada GRD Haiti HTI Jamaica JAM Puerto Rico PRI Saint Kitts and Nevis KNA Saint Lucia LCA Saint Vincent and the Grenadines VCT Trinidad and Tobago TTO Virgin Islands, U.S. VIR 36 XCB Rest of the Caribbean Anguilla AIA Aruba ABW Cayman Islands CYM Cuba CUB Guadeloupe GLP Martinique MTQ Montserrat MSR Netherlands Antilles ANT Turks and Caicos TCA Virgin Islands, British VGB 37 AUT Austria Austria AUT 38 BEL Belgium Belgium BEL 39 DNK Denmark Denmark DNK 40 FIN Finland Finland FIN 41 FRA France France FRA 42 DEU Germany Germany DEU 43 GBR United Kingdom United Kingdom GBR 44 GRC Greece Greece GRC 45 IRL Ireland Ireland IRL 46 ITA Italy Italy ITA 47 LUX Luxembourg Luxembourg LUX 48 NLD Netherlands Netherlands NLD 49 PRT Portugal Portugal PRT 50 ESP Spain Spain ESP 51 SWE Sweden Sweden SWE 52 CHE Switzerland Switzerland CHE 53 XEF Rest of EFTA Iceland ISL Liechtenstein LIE Norway NOR 54 XER Rest of Europe Andorra AND
38
Bosnia and Herzegovina BIH Faroe Islands FRO Gibraltar GIB Macedonia, the former Yugoslav
Republic of MKD
Monaco MCO San Marino SMR Serbia and Montenegro SCG 55 ALB Albania Albania ALB 56 BGR Bulgaria Bulgaria BGR 57 HRV Croatia Croatia HRV 58 CYP Cyprus Cyprus CYP 59 CZE Czech Republic Czech Republic CZE 60 HUN Hungary Hungary HUN 61 MLT Malta Malta MLT 62 POL Poland Poland POL 63 ROM Romania Romania ROM 64 SVK Slovakia Slovakia SVK 65 SVN Slovenia Slovenia SVN 66 EST Estonia Estonia EST 67 LVA Latvia Latvia LVA 68 LTU Lithuania Lithuania LTU 69 RUS Russian Federation Russian Federation RUS 70 XSU Rest of Former Soviet Union Armenia ARM Azerbaijan AZE Belarus BLR Georgia GEO Kazakhstan KAZ Kyrgyzstan KGZ Moldova, Republic of MDA Tajikistan TJK Turkmenistan TKM Ukraine UKR Uzbekistan UZB 71 TUR Turkey Turkey TUR 72 XME Rest of Middle East Bahrain BHR Iran, Islamic Republic of IRN Iraq IRQ Israel ISR Jordan JOR Kuwait KWT Lebanon LBN Palestinian Territory, Occupied PSE
39
Oman OMN Qatar QAT Saudi Arabia SAU Syrian Arab Republic SYR United Arab Emirates ARE Yemen YEM 73 MAR Morocco Morocco MAR 74 TUN Tunisia Tunisia TUN 75 XNF Rest of North Africa Algeria DZA Egypt EGY Libyan Arab Jamahiriya LBY 76 BWA Botswana Botswana BWA 77 ZAF South Africa South Africa ZAF 78 XSC Rest of South African Customs
Union Lesotho LSO
Namibia NAM Swaziland SWZ 79 MWI Malawi Malawi MWI 80 MOZ Mozambique Mozambique MOZ 81 TZA Tanzania Tanzania, United Republic of TZA 82 ZMB Zambia Zambia ZMB 83 ZWE Zimbabwe Zimbabwe ZWE
84 XSD Rest of Southern African
Development Community Angola AGO
Congo, the Democratic Republic
of the COD
Mauritius MUS Seychelles SYC 85 MDG Madagascar Madagascar MDG 86 UGA Uganda Uganda UGA 87 XSS Rest of Sub-Saharan Africa Benin BEN Burkina Faso BFA Burundi BDI Cameroon CMR Cape Verde CPV Central African Republic CAF Chad TCD Comoros COM Congo COG Cote d'Ivoire CIV Djibouti DJI Equatorial Guinea GNQ Eritrea ERI Ethiopia ETH
40
Gabon GAB Gambia GMB Ghana GHA Guinea GIN Guinea-Bissau GNB Kenya KEN Liberia LBR Mali MLI Mauritania MRT Mayotte MYT Niger NER Nigeria NGA Reunion REU Rwanda RWA Saint Helena SHN Sao Tome and Principe STP Senegal SEN Sierra Leone SLE Somalia SOM Sudan SDN Togo TGO